Informatics
Figure 1 Operational metrics in a clinical pathology laboratory4
Sponsors can review preliminary data from their studies and perform early analysis of the results. More and more, sponsors are looking to CROs to provide SEND-compliant datasets to simplify the submission process. For CROs, implementing the systems and processes to produce SEND-compliant datasets can provide an important differentiator in a highly competitive market. Looking beyond the benefits of a common data
model and its ability to provide the required datasets to regulatory agencies in the required for- mat, SEND presents an important opportunity for biopharmaceutical R&D. As data complexity increases and the need for new therapies to meet unmet medical needs continues, SEND datasets, if leveraged properly, have the potential to provide unique operational and R&D insights. Such insights can increase efficiencies, reduce failure rates, and improve safety outcomes in the drug development lifecycle. Considering that one drug can cost an organisation nearly $3 billion to devel- op to market, the leveraging of SEND data is an important means to increasing the return on an existing investment3.
SEND and data mining, visualisation and advanced analytics Example 1: Operational metrics Operational metrics are used by a laboratory to
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gauge the efficiency of its operations over time. An example of operational metrics tracked by a typi- cal clinical pathology lab are illustrated in Figure 1. This visualisation relies upon the count of records in the lab domain (LBTEST) over time (LBDTC) grouped by instrument name and instru- ment location. A convention of using the LBMETHOD raw data to include a concatenation of the instrument name and instrument location makes this possible. A stacked bar chart is then used to show the dis-
tribution of the work among different sites or lab- oratories where the analytical instruments exist. This can be useful to determine over time the workload taken on by each site within the labora- tory network. The Y axis represents the number of tests done in that time period. The time period can be set by the visualisation tool in blocks of differ- ent sizes and a particular time period can be zoomed into in order to be specific to that time period in the analysis. Below this is shown the same information
grouped by instrument. This tells us which type of instrument is responsible for the bulk of the analy- sis during time periods. A trend over time provides information on which additional instruments may be needed to anticipate capacity issues. The visual- isation tools allow one to set the size of the date periods, in order to look at a grosser or finer dis-
Drug Discovery World Winter 2018/19
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